Simplified minimal gated unit variations for recurrent neural networks

@article{Heck2017SimplifiedMG,
  title={Simplified minimal gated unit variations for recurrent neural networks},
  author={Joel Heck and Fathi M. Salem},
  journal={2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)},
  year={2017},
  pages={1593-1596}
}
  • Joel Heck, F. Salem
  • Published 12 January 2017
  • Computer Science, Mathematics
  • 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit which further simplify that design by reducing the number of parameters in the forget-gate dynamic equation. These three… Expand
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